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Found 9 papers, 2 papers with code
Learning Correlated Equilibria in Mean-Field Games
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts.
Learning in Mean Field Games: A Survey
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
Generalization in Mean Field Games by Learning Master Policies
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents.
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Mean-field Games (MFGs) are a continuous approximation of many-agent RL.
Mean Field Games Flock! The Reinforcement Learning Way
We present a method enabling a large number of agents to learn how to flock, which is a natural behavior observed in large populations of animals.
Scaling up Mean Field Games with Online Mirror Descent
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD).
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma$-discounted), allowing in particular for the introduction of an additional common noise.
Machine Learning Optimization Algorithms & Portfolio Allocation
Nevertheless, very few models have succeeded in providing a real alternative solution to the Markowitz model.
